Overview

Dataset statistics

Number of variables47
Number of observations2845342
Missing cells3414349
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory773.4 MiB
Average record size in memory285.0 B

Variable types

Categorical21
Numeric13
Boolean13

Alerts

Country has constant value "US" Constant
Turning_Loop has constant value "False" Constant
ID has a high cardinality: 2845342 distinct values High cardinality
Start_Time has a high cardinality: 1959333 distinct values High cardinality
End_Time has a high cardinality: 2351505 distinct values High cardinality
Description has a high cardinality: 1174563 distinct values High cardinality
Street has a high cardinality: 159651 distinct values High cardinality
City has a high cardinality: 11681 distinct values High cardinality
County has a high cardinality: 1707 distinct values High cardinality
Zipcode has a high cardinality: 363085 distinct values High cardinality
Airport_Code has a high cardinality: 2004 distinct values High cardinality
Weather_Timestamp has a high cardinality: 474214 distinct values High cardinality
Weather_Condition has a high cardinality: 127 distinct values High cardinality
Start_Lat is highly correlated with End_LatHigh correlation
Start_Lng is highly correlated with End_LngHigh correlation
End_Lat is highly correlated with Start_LatHigh correlation
End_Lng is highly correlated with Start_LngHigh correlation
Temperature(F) is highly correlated with Wind_Chill(F)High correlation
Wind_Chill(F) is highly correlated with Temperature(F)High correlation
Bump is highly correlated with Traffic_CalmingHigh correlation
Traffic_Calming is highly correlated with BumpHigh correlation
Start_Lat is highly correlated with End_Lat and 1 other fieldsHigh correlation
Start_Lng is highly correlated with End_LngHigh correlation
End_Lat is highly correlated with Start_Lat and 1 other fieldsHigh correlation
End_Lng is highly correlated with Start_LngHigh correlation
Temperature(F) is highly correlated with Wind_Chill(F)High correlation
Wind_Chill(F) is highly correlated with Start_Lat and 2 other fieldsHigh correlation
Bump is highly correlated with Traffic_CalmingHigh correlation
Traffic_Calming is highly correlated with BumpHigh correlation
Start_Lat is highly correlated with End_LatHigh correlation
Start_Lng is highly correlated with End_LngHigh correlation
End_Lat is highly correlated with Start_LatHigh correlation
End_Lng is highly correlated with Start_LngHigh correlation
Temperature(F) is highly correlated with Wind_Chill(F)High correlation
Wind_Chill(F) is highly correlated with Temperature(F)High correlation
Bump is highly correlated with Traffic_CalmingHigh correlation
Traffic_Calming is highly correlated with BumpHigh correlation
Give_Way is highly correlated with Turning_Loop and 1 other fieldsHigh correlation
State is highly correlated with Turning_Loop and 2 other fieldsHigh correlation
Severity is highly correlated with Turning_Loop and 1 other fieldsHigh correlation
Civil_Twilight is highly correlated with Turning_Loop and 4 other fieldsHigh correlation
Side is highly correlated with Turning_Loop and 1 other fieldsHigh correlation
Bump is highly correlated with Turning_Loop and 2 other fieldsHigh correlation
Crossing is highly correlated with Turning_Loop and 1 other fieldsHigh correlation
Railway is highly correlated with Turning_Loop and 1 other fieldsHigh correlation
Amenity is highly correlated with Turning_Loop and 1 other fieldsHigh correlation
Stop is highly correlated with Turning_Loop and 1 other fieldsHigh correlation
Turning_Loop is highly correlated with Give_Way and 21 other fieldsHigh correlation
Nautical_Twilight is highly correlated with Civil_Twilight and 4 other fieldsHigh correlation
Junction is highly correlated with Turning_Loop and 1 other fieldsHigh correlation
Wind_Direction is highly correlated with Turning_Loop and 1 other fieldsHigh correlation
Roundabout is highly correlated with Turning_Loop and 1 other fieldsHigh correlation
Traffic_Calming is highly correlated with Bump and 2 other fieldsHigh correlation
Astronomical_Twilight is highly correlated with Civil_Twilight and 4 other fieldsHigh correlation
No_Exit is highly correlated with Turning_Loop and 1 other fieldsHigh correlation
Sunrise_Sunset is highly correlated with Civil_Twilight and 4 other fieldsHigh correlation
Traffic_Signal is highly correlated with Turning_Loop and 1 other fieldsHigh correlation
Station is highly correlated with Turning_Loop and 1 other fieldsHigh correlation
Timezone is highly correlated with State and 2 other fieldsHigh correlation
Country is highly correlated with Give_Way and 21 other fieldsHigh correlation
Start_Lat is highly correlated with Start_Lng and 5 other fieldsHigh correlation
Start_Lng is highly correlated with Start_Lat and 4 other fieldsHigh correlation
End_Lat is highly correlated with Start_Lat and 5 other fieldsHigh correlation
End_Lng is highly correlated with Start_Lat and 4 other fieldsHigh correlation
State is highly correlated with Start_Lat and 6 other fieldsHigh correlation
Timezone is highly correlated with Start_Lat and 4 other fieldsHigh correlation
Temperature(F) is highly correlated with Wind_Chill(F)High correlation
Wind_Chill(F) is highly correlated with Start_Lat and 3 other fieldsHigh correlation
Pressure(in) is highly correlated with StateHigh correlation
Bump is highly correlated with Traffic_CalmingHigh correlation
Crossing is highly correlated with Traffic_SignalHigh correlation
Traffic_Calming is highly correlated with BumpHigh correlation
Traffic_Signal is highly correlated with CrossingHigh correlation
Sunrise_Sunset is highly correlated with Civil_Twilight and 2 other fieldsHigh correlation
Civil_Twilight is highly correlated with Sunrise_Sunset and 2 other fieldsHigh correlation
Nautical_Twilight is highly correlated with Sunrise_Sunset and 2 other fieldsHigh correlation
Astronomical_Twilight is highly correlated with Sunrise_Sunset and 2 other fieldsHigh correlation
Number has 1743911 (61.3%) missing values Missing
Weather_Timestamp has 50736 (1.8%) missing values Missing
Temperature(F) has 69274 (2.4%) missing values Missing
Wind_Chill(F) has 469643 (16.5%) missing values Missing
Humidity(%) has 73092 (2.6%) missing values Missing
Pressure(in) has 59200 (2.1%) missing values Missing
Visibility(mi) has 70546 (2.5%) missing values Missing
Wind_Direction has 73775 (2.6%) missing values Missing
Wind_Speed(mph) has 157944 (5.6%) missing values Missing
Precipitation(in) has 549458 (19.3%) missing values Missing
Weather_Condition has 70636 (2.5%) missing values Missing
Number is highly skewed (γ1 = 156.9450181) Skewed
Precipitation(in) is highly skewed (γ1 = 106.2589449) Skewed
ID is uniformly distributed Uniform
End_Time is uniformly distributed Uniform
ID has unique values Unique
Distance(mi) has 385441 (13.5%) zeros Zeros
Wind_Speed(mph) has 433636 (15.2%) zeros Zeros
Precipitation(in) has 2104242 (74.0%) zeros Zeros

Reproduction

Analysis started2022-05-28 08:50:38.659229
Analysis finished2022-05-28 09:19:30.860048
Duration28 minutes and 52.2 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

ID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct2845342
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
A-1
 
1
A-1896898
 
1
A-1896890
 
1
A-1896891
 
1
A-1896892
 
1
Other values (2845337)
2845337 

Length

Max length9
Median length9
Mean length8.609500721
Min length3

Characters and Unicode

Total characters24496974
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2845342 ?
Unique (%)100.0%

Sample

1st rowA-1
2nd rowA-2
3rd rowA-3
4th rowA-4
5th rowA-5

Common Values

ValueCountFrequency (%)
A-11
 
< 0.1%
A-18968981
 
< 0.1%
A-18968901
 
< 0.1%
A-18968911
 
< 0.1%
A-18968921
 
< 0.1%
A-18968931
 
< 0.1%
A-18968941
 
< 0.1%
A-18968951
 
< 0.1%
A-18968961
 
< 0.1%
A-18968971
 
< 0.1%
Other values (2845332)2845332
> 99.9%

Length

2022-05-28T12:19:31.550138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-11
 
< 0.1%
a-61
 
< 0.1%
a-421
 
< 0.1%
a-241
 
< 0.1%
a-221
 
< 0.1%
a-861
 
< 0.1%
a-111
 
< 0.1%
a-31
 
< 0.1%
a-41
 
< 0.1%
a-51
 
< 0.1%
Other values (2845332)2845332
> 99.9%

Most occurring characters

ValueCountFrequency (%)
A2845342
11.6%
-2845342
11.6%
12728675
11.1%
22574018
10.5%
31728617
7.1%
41723910
7.0%
51717907
7.0%
61717564
7.0%
71717564
7.0%
81662907
6.8%
Other values (2)3235128
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18806290
76.8%
Uppercase Letter2845342
 
11.6%
Dash Punctuation2845342
 
11.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12728675
14.5%
22574018
13.7%
31728617
9.2%
41723910
9.2%
51717907
9.1%
61717564
9.1%
71717564
9.1%
81662907
8.8%
91617564
8.6%
01617564
8.6%
Uppercase Letter
ValueCountFrequency (%)
A2845342
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2845342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21651632
88.4%
Latin2845342
 
11.6%

Most frequent character per script

Common
ValueCountFrequency (%)
-2845342
13.1%
12728675
12.6%
22574018
11.9%
31728617
8.0%
41723910
8.0%
51717907
7.9%
61717564
7.9%
71717564
7.9%
81662907
7.7%
91617564
7.5%
Latin
ValueCountFrequency (%)
A2845342
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII24496974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A2845342
11.6%
-2845342
11.6%
12728675
11.1%
22574018
10.5%
31728617
7.1%
41723910
7.0%
51717907
7.0%
61717564
7.0%
71717564
7.0%
81662907
6.8%
Other values (2)3235128
13.2%

Severity
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
2
2532991 
3
 
155105
4
 
131193
1
 
26053

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2845342
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
22532991
89.0%
3155105
 
5.5%
4131193
 
4.6%
126053
 
0.9%

Length

2022-05-28T12:19:33.050818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-28T12:19:33.310134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
22532991
89.0%
3155105
 
5.5%
4131193
 
4.6%
126053
 
0.9%

Most occurring characters

ValueCountFrequency (%)
22532991
89.0%
3155105
 
5.5%
4131193
 
4.6%
126053
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2845342
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
22532991
89.0%
3155105
 
5.5%
4131193
 
4.6%
126053
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common2845342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
22532991
89.0%
3155105
 
5.5%
4131193
 
4.6%
126053
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2845342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22532991
89.0%
3155105
 
5.5%
4131193
 
4.6%
126053
 
0.9%

Start_Time
Categorical

HIGH CARDINALITY

Distinct1959333
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
2021-01-26 16:16:13
 
214
2021-01-26 16:17:33
 
150
2021-02-16 06:42:43
 
130
2021-05-03 06:29:42
 
92
2021-04-26 08:58:47
 
87
Other values (1959328)
2844669 

Length

Max length29
Median length19
Mean length19.97513621
Min length19

Characters and Unicode

Total characters56836094
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1474489 ?
Unique (%)51.8%

Sample

1st row2016-02-08 00:37:08
2nd row2016-02-08 05:56:20
3rd row2016-02-08 06:15:39
4th row2016-02-08 06:51:45
5th row2016-02-08 07:53:43

Common Values

ValueCountFrequency (%)
2021-01-26 16:16:13214
 
< 0.1%
2021-01-26 16:17:33150
 
< 0.1%
2021-02-16 06:42:43130
 
< 0.1%
2021-05-03 06:29:4292
 
< 0.1%
2021-04-26 08:58:4787
 
< 0.1%
2021-02-16 06:43:3585
 
< 0.1%
2021-11-21 18:37:5184
 
< 0.1%
2020-12-16 13:53:2576
 
< 0.1%
2021-04-14 13:51:3067
 
< 0.1%
2021-05-03 06:30:2866
 
< 0.1%
Other values (1959323)2844291
> 99.9%

Length

2022-05-28T12:19:33.723038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-12-2312904
 
0.2%
2021-12-1711505
 
0.2%
2021-12-1011014
 
0.2%
2021-12-1510603
 
0.2%
2021-12-1610362
 
0.2%
2021-12-1410298
 
0.2%
2021-12-0310282
 
0.2%
2021-12-3010171
 
0.2%
2021-12-229785
 
0.2%
2021-12-079434
 
0.2%
Other values (151175)5584326
98.1%

Most occurring characters

ValueCountFrequency (%)
013777809
24.2%
28658816
15.2%
18255915
14.5%
-5690684
10.0%
:5690684
10.0%
2845342
 
5.0%
32299501
 
4.0%
51906020
 
3.4%
41856694
 
3.3%
91436136
 
2.5%
Other values (4)4418493
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number42327032
74.5%
Other Punctuation5973036
 
10.5%
Dash Punctuation5690684
 
10.0%
Space Separator2845342
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013777809
32.6%
28658816
20.5%
18255915
19.5%
32299501
 
5.4%
51906020
 
4.5%
41856694
 
4.4%
91436136
 
3.4%
61392070
 
3.3%
71390217
 
3.3%
81353854
 
3.2%
Other Punctuation
ValueCountFrequency (%)
:5690684
95.3%
.282352
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
-5690684
100.0%
Space Separator
ValueCountFrequency (%)
2845342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common56836094
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
013777809
24.2%
28658816
15.2%
18255915
14.5%
-5690684
10.0%
:5690684
10.0%
2845342
 
5.0%
32299501
 
4.0%
51906020
 
3.4%
41856694
 
3.3%
91436136
 
2.5%
Other values (4)4418493
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII56836094
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
013777809
24.2%
28658816
15.2%
18255915
14.5%
-5690684
10.0%
:5690684
10.0%
2845342
 
5.0%
32299501
 
4.0%
51906020
 
3.4%
41856694
 
3.3%
91436136
 
2.5%
Other values (4)4418493
 
7.8%

End_Time
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2351505
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
2021-11-22 08:00:00
 
88
2017-05-15 15:22:55
 
58
2019-10-26 09:14:51
 
47
2020-02-14 00:00:00
 
46
2020-02-12 00:00:00
 
42
Other values (2351500)
2845061 

Length

Max length29
Median length19
Mean length19.97513621
Min length19

Characters and Unicode

Total characters56836094
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2000679 ?
Unique (%)70.3%

Sample

1st row2016-02-08 06:37:08
2nd row2016-02-08 11:56:20
3rd row2016-02-08 12:15:39
4th row2016-02-08 12:51:45
5th row2016-02-08 13:53:43

Common Values

ValueCountFrequency (%)
2021-11-22 08:00:0088
 
< 0.1%
2017-05-15 15:22:5558
 
< 0.1%
2019-10-26 09:14:5147
 
< 0.1%
2020-02-14 00:00:0046
 
< 0.1%
2020-02-12 00:00:0042
 
< 0.1%
2020-01-25 00:00:0041
 
< 0.1%
2021-02-13 16:45:0039
 
< 0.1%
2020-02-15 00:00:0038
 
< 0.1%
2020-02-07 00:00:0038
 
< 0.1%
2020-02-11 00:00:0037
 
< 0.1%
Other values (2351495)2844868
> 99.9%

Length

2022-05-28T12:19:34.183177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-12-2312569
 
0.2%
2021-12-1711473
 
0.2%
2021-12-1010781
 
0.2%
2021-12-1510619
 
0.2%
2021-12-1610342
 
0.2%
2021-12-1410307
 
0.2%
2021-12-3010138
 
0.2%
2021-12-0310092
 
0.2%
2021-12-229783
 
0.2%
2021-12-079500
 
0.2%
Other values (175401)5585080
98.1%

Most occurring characters

ValueCountFrequency (%)
012296795
21.6%
29101230
16.0%
18427131
14.8%
-5690684
10.0%
:5690684
10.0%
2845342
 
5.0%
32452424
 
4.3%
52090696
 
3.7%
42067261
 
3.6%
91604270
 
2.8%
Other values (4)4569577
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number42327032
74.5%
Other Punctuation5973036
 
10.5%
Dash Punctuation5690684
 
10.0%
Space Separator2845342
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
012296795
29.1%
29101230
21.5%
18427131
19.9%
32452424
 
5.8%
52090696
 
4.9%
42067261
 
4.9%
91604270
 
3.8%
81467902
 
3.5%
71415879
 
3.3%
61403444
 
3.3%
Other Punctuation
ValueCountFrequency (%)
:5690684
95.3%
.282352
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
-5690684
100.0%
Space Separator
ValueCountFrequency (%)
2845342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common56836094
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
012296795
21.6%
29101230
16.0%
18427131
14.8%
-5690684
10.0%
:5690684
10.0%
2845342
 
5.0%
32452424
 
4.3%
52090696
 
3.7%
42067261
 
3.6%
91604270
 
2.8%
Other values (4)4569577
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII56836094
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
012296795
21.6%
29101230
16.0%
18427131
14.8%
-5690684
10.0%
:5690684
10.0%
2845342
 
5.0%
32452424
 
4.3%
52090696
 
3.7%
42067261
 
3.6%
91604270
 
2.8%
Other values (4)4569577
 
8.0%

Start_Lat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1093618
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.24520054
Minimum24.566027
Maximum49.00058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2022-05-28T12:19:34.430698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum24.566027
5-th percentile25.96125525
Q133.445174
median36.098609
Q340.160243
95-th percentile45.0898267
Maximum49.00058
Range24.434553
Interquartile range (IQR)6.715069

Descriptive statistics

Standard deviation5.36379746
Coefficient of variation (CV)0.1479864197
Kurtosis-0.5870620559
Mean36.24520054
Median Absolute Deviation (MAD)3.376716
Skewness-0.1146398312
Sum103129991.4
Variance28.77032319
MonotonicityNot monotonic
2022-05-28T12:19:34.693494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.702455348
 
< 0.1%
27.447751302
 
< 0.1%
28.452192285
 
< 0.1%
28.449924268
 
< 0.1%
40.85306255
 
< 0.1%
25.689146254
 
< 0.1%
25.712548238
 
< 0.1%
34.020212235
 
< 0.1%
28.452939231
 
< 0.1%
25.810617227
 
< 0.1%
Other values (1093608)2842699
99.9%
ValueCountFrequency (%)
24.5660271
 
< 0.1%
24.5700871
 
< 0.1%
24.5702221
 
< 0.1%
24.570331
 
< 0.1%
24.5705841
 
< 0.1%
24.5712023
< 0.1%
24.571241
 
< 0.1%
24.5713081
 
< 0.1%
24.571311
 
< 0.1%
24.5715361
 
< 0.1%
ValueCountFrequency (%)
49.000581
 
< 0.1%
49.000561
 
< 0.1%
49.0002691
 
< 0.1%
49.000261
 
< 0.1%
48.999511
 
< 0.1%
48.9984453
< 0.1%
48.998381
 
< 0.1%
48.9974571
 
< 0.1%
48.9965391
 
< 0.1%
48.9960141
 
< 0.1%

Start_Lng
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1120365
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-97.11463289
Minimum-124.548074
Maximum-67.113167
Zeros0
Zeros (%)0.0%
Negative2845342
Negative (%)100.0%
Memory size21.7 MiB
2022-05-28T12:19:34.963548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-124.548074
5-th percentile-122.32597
Q1-118.0331125
median-92.418076
Q3-80.372431
95-th percentile-74.174027
Maximum-67.113167
Range57.434907
Interquartile range (IQR)37.6606815

Descriptive statistics

Standard deviation18.31781912
Coefficient of variation (CV)-0.1886205876
Kurtosis-1.649460042
Mean-97.11463289
Median Absolute Deviation (MAD)15.437108
Skewness-0.2388874032
Sum-276324343.8
Variance335.5424974
MonotonicityNot monotonic
2022-05-28T12:19:35.198459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-80.332105348
 
< 0.1%
-81.400388285
 
< 0.1%
-81.479136268
 
< 0.1%
-80.382872254
 
< 0.1%
-73.96011248
 
< 0.1%
-80.382427238
 
< 0.1%
-117.814998235
 
< 0.1%
-81.400159231
 
< 0.1%
-77.478828218
 
< 0.1%
-82.573305216
 
< 0.1%
Other values (1120355)2842801
99.9%
ValueCountFrequency (%)
-124.5480742
 
< 0.1%
-124.5177441
 
< 0.1%
-124.5119491
 
< 0.1%
-124.4975851
 
< 0.1%
-124.4975672
 
< 0.1%
-124.497471
 
< 0.1%
-124.4974482
 
< 0.1%
-124.4974381
 
< 0.1%
-124.497425
< 0.1%
-124.497411
 
< 0.1%
ValueCountFrequency (%)
-67.1131671
< 0.1%
-67.4035511
< 0.1%
-67.484131
< 0.1%
-67.6068641
< 0.1%
-67.6068751
< 0.1%
-67.6143871
< 0.1%
-67.6265761
< 0.1%
-67.703371
< 0.1%
-67.7396961
< 0.1%
-67.787341
< 0.1%

End_Lat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1080811
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.24532079
Minimum24.566013
Maximum49.075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2022-05-28T12:19:35.479093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum24.566013
5-th percentile25.958574
Q133.446278
median36.0979865
Q340.16104925
95-th percentile45.0905195
Maximum49.075
Range24.508987
Interquartile range (IQR)6.71477125

Descriptive statistics

Standard deviation5.363872991
Coefficient of variation (CV)0.1479880126
Kurtosis-0.5870021244
Mean36.24532079
Median Absolute Deviation (MAD)3.3769205
Skewness-0.1146570877
Sum103130333.6
Variance28.77113347
MonotonicityNot monotonic
2022-05-28T12:19:35.758786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.701774755
 
< 0.1%
25.684322709
 
< 0.1%
25.73316589
 
< 0.1%
28.449928586
 
< 0.1%
28.45019581
 
< 0.1%
25.924771576
 
< 0.1%
28.450015567
 
< 0.1%
25.686252548
 
< 0.1%
37.55119525
 
< 0.1%
35.842775522
 
< 0.1%
Other values (1080801)2839384
99.8%
ValueCountFrequency (%)
24.5660131
< 0.1%
24.5701071
< 0.1%
24.570111
< 0.1%
24.570181
< 0.1%
24.570361
< 0.1%
24.5704611
< 0.1%
24.571241
< 0.1%
24.571261
< 0.1%
24.5713091
< 0.1%
24.5713891
< 0.1%
ValueCountFrequency (%)
49.0751
 
< 0.1%
49.002141
 
< 0.1%
49.000763
< 0.1%
49.000561
 
< 0.1%
48.9999221
 
< 0.1%
48.999281
 
< 0.1%
48.9991571
 
< 0.1%
48.9991321
 
< 0.1%
48.998991
 
< 0.1%
48.9989012
< 0.1%

End_Lng
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1105404
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-97.1143871
Minimum-124.545748
Maximum-67.109242
Zeros0
Zeros (%)0.0%
Negative2845342
Negative (%)100.0%
Memory size21.7 MiB
2022-05-28T12:19:36.007062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-124.545748
5-th percentile-122.32599
Q1-118.0333308
median-92.417718
Q3-80.373383
95-th percentile-74.173778
Maximum-67.109242
Range57.436506
Interquartile range (IQR)37.65994775

Descriptive statistics

Standard deviation18.31763242
Coefficient of variation (CV)-0.1886191426
Kurtosis-1.649469867
Mean-97.1143871
Median Absolute Deviation (MAD)15.437678
Skewness-0.2388887569
Sum-276323644.4
Variance335.5356576
MonotonicityNot monotonic
2022-05-28T12:19:36.213347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-80.334179755
 
< 0.1%
-80.416621709
 
< 0.1%
-80.336612589
 
< 0.1%
-81.477219586
 
< 0.1%
-81.399777583
 
< 0.1%
-80.293318576
 
< 0.1%
-81.471375565
 
< 0.1%
-80.416521547
 
< 0.1%
-77.475128525
 
< 0.1%
-78.680237522
 
< 0.1%
Other values (1105394)2839385
99.8%
ValueCountFrequency (%)
-124.5457482
 
< 0.1%
-124.5092631
 
< 0.1%
-124.4978291
 
< 0.1%
-124.4974782
 
< 0.1%
-124.497471
 
< 0.1%
-124.4974382
 
< 0.1%
-124.4974211
 
< 0.1%
-124.4974195
< 0.1%
-124.497411
 
< 0.1%
-124.4973571
 
< 0.1%
ValueCountFrequency (%)
-67.1092421
< 0.1%
-67.403551
< 0.1%
-67.484131
< 0.1%
-67.6068641
< 0.1%
-67.620341
< 0.1%
-67.6265761
< 0.1%
-67.6266051
< 0.1%
-67.7064481
< 0.1%
-67.7398171
< 0.1%
-67.787341
< 0.1%

Distance(mi)
Real number (ℝ≥0)

ZEROS

Distinct14165
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7026778946
Minimum0
Maximum155.186
Zeros385441
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2022-05-28T12:19:36.465766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.052
median0.244
Q30.764
95-th percentile2.894
Maximum155.186
Range155.186
Interquartile range (IQR)0.712

Descriptive statistics

Standard deviation1.560360825
Coefficient of variation (CV)2.220591878
Kurtosis806.7243696
Mean0.7026778946
Median Absolute Deviation (MAD)0.238
Skewness16.67083479
Sum1999358.926
Variance2.434725905
MonotonicityNot monotonic
2022-05-28T12:19:36.693105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0385441
 
13.5%
0.0089262
 
0.3%
0.0098978
 
0.3%
0.018737
 
0.3%
0.0077846
 
0.3%
0.0117319
 
0.3%
0.036937
 
0.2%
0.0126894
 
0.2%
0.0286789
 
0.2%
0.0246777
 
0.2%
Other values (14155)2390362
84.0%
ValueCountFrequency (%)
0385441
13.5%
0.0014897
 
0.2%
0.0022605
 
0.1%
0.0033388
 
0.1%
0.0044670
 
0.2%
0.0055608
 
0.2%
0.0066687
 
0.2%
0.0077846
 
0.3%
0.0089262
 
0.3%
0.0098978
 
0.3%
ValueCountFrequency (%)
155.1861
< 0.1%
153.6631
< 0.1%
152.5431
< 0.1%
151.5251
< 0.1%
150.1381
< 0.1%
149.691
< 0.1%
149.6871
< 0.1%
143.2421
< 0.1%
138.5621
< 0.1%
137.6181
< 0.1%

Description
Categorical

HIGH CARDINALITY

Distinct1174563
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
A crash has occurred causing no to minimum delays. Use caution.
 
7978
A crash has occurred use caution.
 
2531
An unconfirmed report of a crash has been received. Use caution.
 
2308
Hazardous debris is causing no to minimum delays. Use caution.
 
2095
At I-15 - Accident.
 
2070
Other values (1174558)
2828360 

Length

Max length577
Median length375
Mean length66.62176919
Min length2

Characters and Unicode

Total characters189561718
Distinct characters98
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique790550 ?
Unique (%)27.8%

Sample

1st rowBetween Sawmill Rd/Exit 20 and OH-315/Olentangy Riv Rd/Exit 22 - Accident.
2nd rowAt OH-4/OH-235/Exit 41 - Accident.
3rd rowAt I-71/US-50/Exit 1 - Accident.
4th rowAt Dart Ave/Exit 21 - Accident.
5th rowAt Mitchell Ave/Exit 6 - Accident.

Common Values

ValueCountFrequency (%)
A crash has occurred causing no to minimum delays. Use caution.7978
 
0.3%
A crash has occurred use caution.2531
 
0.1%
An unconfirmed report of a crash has been received. Use caution.2308
 
0.1%
Hazardous debris is causing no to minimum delays. Use caution.2095
 
0.1%
At I-15 - Accident.2070
 
0.1%
A disabled vehicle is creating a hazard causing no to minimum delays. Use caution.1912
 
0.1%
At I-5 - Accident.1907
 
0.1%
At I-405/San Diego Fwy - Accident.1769
 
0.1%
At I-605 - Accident.1486
 
0.1%
Incident on I-95 NB near I-95 Drive with caution.1304
 
< 0.1%
Other values (1174553)2819982
99.1%

Length

2022-05-28T12:19:37.175468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
accident1798430
 
5.3%
to1650005
 
4.9%
on1598467
 
4.8%
1564037
 
4.7%
at916875
 
2.7%
near878801
 
2.6%
incident856768
 
2.5%
from796136
 
2.4%
due782840
 
2.3%
rd735640
 
2.2%
Other values (162605)22044277
65.6%

Most occurring characters

ValueCountFrequency (%)
30776871
 
16.2%
t11717120
 
6.2%
e10644059
 
5.6%
n9885855
 
5.2%
o8424876
 
4.4%
i8301886
 
4.4%
a7363979
 
3.9%
c7074776
 
3.7%
d6570628
 
3.5%
r5919544
 
3.1%
Other values (88)82882124
43.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter98347096
51.9%
Uppercase Letter34299454
 
18.1%
Space Separator30777852
 
16.2%
Decimal Number12986970
 
6.9%
Other Punctuation5243233
 
2.8%
Dash Punctuation5217447
 
2.8%
Open Punctuation1344559
 
0.7%
Close Punctuation1344492
 
0.7%
Math Symbol365
 
< 0.1%
Modifier Symbol102
 
< 0.1%
Other values (5)148
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t11717120
11.9%
e10644059
10.8%
n9885855
10.1%
o8424876
8.6%
i8301886
8.4%
a7363979
 
7.5%
c7074776
 
7.2%
d6570628
 
6.7%
r5919544
 
6.0%
l3533392
 
3.6%
Other values (19)18910981
19.2%
Uppercase Letter
ValueCountFrequency (%)
A4238891
12.4%
S3746799
 
10.9%
I3061578
 
8.9%
E2940823
 
8.6%
R2604603
 
7.6%
D1817116
 
5.3%
N1709487
 
5.0%
C1555252
 
4.5%
L1501320
 
4.4%
B1449485
 
4.2%
Other values (16)9674100
28.2%
Other Punctuation
ValueCountFrequency (%)
.3211573
61.3%
/1897673
36.2%
:65712
 
1.3%
'26434
 
0.5%
;25138
 
0.5%
*7522
 
0.1%
&5856
 
0.1%
#2864
 
0.1%
@305
 
< 0.1%
?92
 
< 0.1%
Other values (4)64
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
12269949
17.5%
51571097
12.1%
21555128
12.0%
01459676
11.2%
41186461
9.1%
91130460
8.7%
31026620
7.9%
6945893
7.3%
8940013
7.2%
7901673
 
6.9%
Open Punctuation
ValueCountFrequency (%)
(1239926
92.2%
[101888
 
7.6%
{2745
 
0.2%
Close Punctuation
ValueCountFrequency (%)
)1239866
92.2%
]101881
 
7.6%
}2745
 
0.2%
Math Symbol
ValueCountFrequency (%)
+161
44.1%
=135
37.0%
~69
18.9%
Space Separator
ValueCountFrequency (%)
30776871
> 99.9%
 981
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
^100
98.0%
`2
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
-5217447
100.0%
Control
ValueCountFrequency (%)
96
100.0%
Connector Punctuation
ValueCountFrequency (%)
_42
100.0%
Currency Symbol
ValueCountFrequency (%)
$6
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin132646550
70.0%
Common56915168
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t11717120
 
8.8%
e10644059
 
8.0%
n9885855
 
7.5%
o8424876
 
6.4%
i8301886
 
6.3%
a7363979
 
5.6%
c7074776
 
5.3%
d6570628
 
5.0%
r5919544
 
4.5%
A4238891
 
3.2%
Other values (45)52504936
39.6%
Common
ValueCountFrequency (%)
30776871
54.1%
-5217447
 
9.2%
.3211573
 
5.6%
12269949
 
4.0%
/1897673
 
3.3%
51571097
 
2.8%
21555128
 
2.7%
01459676
 
2.6%
(1239926
 
2.2%
)1239866
 
2.2%
Other values (33)6475962
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII189560715
> 99.9%
None997
 
< 0.1%
Punctuation4
 
< 0.1%
Specials2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30776871
 
16.2%
t11717120
 
6.2%
e10644059
 
5.6%
n9885855
 
5.2%
o8424876
 
4.4%
i8301886
 
4.4%
a7363979
 
3.9%
c7074776
 
3.7%
d6570628
 
3.5%
r5919544
 
3.1%
Other values (81)82881121
43.7%
None
ValueCountFrequency (%)
 981
98.4%
ñ8
 
0.8%
é7
 
0.7%
í1
 
0.1%
Punctuation
ValueCountFrequency (%)
2
50.0%
2
50.0%
Specials
ValueCountFrequency (%)
2
100.0%

Number
Real number (ℝ≥0)

MISSING
SKEWED

Distinct46402
Distinct (%)4.2%
Missing1743911
Missing (%)61.3%
Infinite0
Infinite (%)0.0%
Mean8089.408114
Minimum0
Maximum9999997
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2022-05-28T12:19:37.460055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile108
Q11270
median4007
Q39567
95-th percentile28661.5
Maximum9999997
Range9999997
Interquartile range (IQR)8297

Descriptive statistics

Standard deviation18360.09399
Coefficient of variation (CV)2.269646152
Kurtosis79967.464
Mean8089.408114
Median Absolute Deviation (MAD)3309
Skewness156.9450181
Sum8909924868
Variance337093051.5
MonotonicityNot monotonic
2022-05-28T12:19:37.695716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110662
 
0.4%
28747
 
0.3%
1016706
 
0.2%
1006187
 
0.2%
1982617
 
0.1%
2982270
 
0.1%
2002228
 
0.1%
2012224
 
0.1%
3982191
 
0.1%
1992116
 
0.1%
Other values (46392)1055483
37.1%
(Missing)1743911
61.3%
ValueCountFrequency (%)
01
 
< 0.1%
110662
0.4%
28747
0.3%
3622
 
< 0.1%
4557
 
< 0.1%
5336
 
< 0.1%
6306
 
< 0.1%
7256
 
< 0.1%
8301
 
< 0.1%
9269
 
< 0.1%
ValueCountFrequency (%)
99999971
< 0.1%
9610611
< 0.1%
9610521
< 0.1%
9610512
< 0.1%
9610431
< 0.1%
9610051
< 0.1%
9425011
< 0.1%
9419961
< 0.1%
9408841
< 0.1%
8525641
< 0.1%

Street
Categorical

HIGH CARDINALITY

Distinct159651
Distinct (%)5.6%
Missing2
Missing (%)< 0.1%
Memory size21.7 MiB
I-95 N
 
39853
I-5 N
 
39402
I-95 S
 
36425
I-5 S
 
30229
I-10 E
 
26164
Other values (159646)
2673267 

Length

Max length51
Median length46
Mean length11.16221682
Min length2

Characters and Unicode

Total characters31760302
Distinct characters72
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64154 ?
Unique (%)2.3%

Sample

1st rowOuterbelt E
2nd rowI-70 E
3rd rowI-75 S
4th rowI-77 N
5th rowI-75 S

Common Values

ValueCountFrequency (%)
I-95 N39853
 
1.4%
I-5 N39402
 
1.4%
I-95 S36425
 
1.3%
I-5 S30229
 
1.1%
I-10 E26164
 
0.9%
I-10 W25298
 
0.9%
I-80 W17545
 
0.6%
I-80 E16873
 
0.6%
I-405 N13708
 
0.5%
I-15 N12675
 
0.4%
Other values (159641)2587168
90.9%

Length

2022-05-28T12:19:37.988033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n514849
 
7.1%
s512084
 
7.1%
w394072
 
5.5%
e392955
 
5.5%
rd344344
 
4.8%
ave228318
 
3.2%
st215172
 
3.0%
fwy149795
 
2.1%
highway149101
 
2.1%
blvd112666
 
1.6%
Other values (43749)4195437
58.2%

Most occurring characters

ValueCountFrequency (%)
5464883
 
17.2%
e1620891
 
5.1%
a1390354
 
4.4%
S1151559
 
3.6%
t1124812
 
3.5%
-1076858
 
3.4%
r1050604
 
3.3%
o1041312
 
3.3%
n1002552
 
3.2%
l939981
 
3.0%
Other values (62)15896496
50.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14683462
46.2%
Uppercase Letter7354331
23.2%
Space Separator5464883
 
17.2%
Decimal Number3163551
 
10.0%
Dash Punctuation1076858
 
3.4%
Other Punctuation16875
 
0.1%
Other Symbol342
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1620891
11.0%
a1390354
 
9.5%
t1124812
 
7.7%
r1050604
 
7.2%
o1041312
 
7.1%
n1002552
 
6.8%
l939981
 
6.4%
i913631
 
6.2%
d897396
 
6.1%
y797274
 
5.4%
Other values (18)3904655
26.6%
Uppercase Letter
ValueCountFrequency (%)
S1151559
15.7%
I819857
11.1%
N655079
8.9%
W606436
 
8.2%
E556151
 
7.6%
A488039
 
6.6%
R479994
 
6.5%
C373251
 
5.1%
H361322
 
4.9%
B313233
 
4.3%
Other values (16)1549410
21.1%
Decimal Number
ValueCountFrequency (%)
5493665
15.6%
0451804
14.3%
1442418
14.0%
9319319
10.1%
4295166
9.3%
2266844
8.4%
8262531
8.3%
7229622
7.3%
6227316
7.2%
3174866
 
5.5%
Other Punctuation
ValueCountFrequency (%)
'11457
67.9%
.4230
 
25.1%
/1186
 
7.0%
&1
 
< 0.1%
;1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5464883
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1076858
100.0%
Other Symbol
ValueCountFrequency (%)
342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22037793
69.4%
Common9722509
30.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1620891
 
7.4%
a1390354
 
6.3%
S1151559
 
5.2%
t1124812
 
5.1%
r1050604
 
4.8%
o1041312
 
4.7%
n1002552
 
4.5%
l939981
 
4.3%
i913631
 
4.1%
d897396
 
4.1%
Other values (44)10904701
49.5%
Common
ValueCountFrequency (%)
5464883
56.2%
-1076858
 
11.1%
5493665
 
5.1%
0451804
 
4.6%
1442418
 
4.6%
9319319
 
3.3%
4295166
 
3.0%
2266844
 
2.7%
8262531
 
2.7%
7229622
 
2.4%
Other values (8)419399
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII31759462
> 99.9%
None498
 
< 0.1%
Specials342
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5464883
 
17.2%
e1620891
 
5.1%
a1390354
 
4.4%
S1151559
 
3.6%
t1124812
 
3.5%
-1076858
 
3.4%
r1050604
 
3.3%
o1041312
 
3.3%
n1002552
 
3.2%
l939981
 
3.0%
Other values (59)15895656
50.1%
None
ValueCountFrequency (%)
é489
98.2%
ñ9
 
1.8%
Specials
ValueCountFrequency (%)
342
100.0%

Side
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
R
2353309 
L
492032 
N
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2845342
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R2353309
82.7%
L492032
 
17.3%
N1
 
< 0.1%

Length

2022-05-28T12:19:38.188605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-28T12:19:38.513472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
r2353309
82.7%
l492032
 
17.3%
n1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
R2353309
82.7%
L492032
 
17.3%
N1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2845342
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R2353309
82.7%
L492032
 
17.3%
N1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin2845342
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R2353309
82.7%
L492032
 
17.3%
N1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2845342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R2353309
82.7%
L492032
 
17.3%
N1
 
< 0.1%

City
Categorical

HIGH CARDINALITY

Distinct11681
Distinct (%)0.4%
Missing137
Missing (%)< 0.1%
Memory size21.7 MiB
Miami
 
106966
Los Angeles
 
68956
Orlando
 
54691
Dallas
 
41979
Houston
 
39448
Other values (11676)
2533165 

Length

Max length30
Median length26
Mean length8.810501177
Min length3

Characters and Unicode

Total characters25067682
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1110 ?
Unique (%)< 0.1%

Sample

1st rowDublin
2nd rowDayton
3rd rowCincinnati
4th rowAkron
5th rowCincinnati

Common Values

ValueCountFrequency (%)
Miami106966
 
3.8%
Los Angeles68956
 
2.4%
Orlando54691
 
1.9%
Dallas41979
 
1.5%
Houston39448
 
1.4%
Charlotte33152
 
1.2%
Sacramento32559
 
1.1%
San Diego26627
 
0.9%
Raleigh22840
 
0.8%
Minneapolis22768
 
0.8%
Other values (11671)2395219
84.2%

Length

2022-05-28T12:19:38.677978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
miami119442
 
3.2%
san87961
 
2.4%
los74220
 
2.0%
city69201
 
1.9%
angeles69027
 
1.9%
orlando54691
 
1.5%
dallas42171
 
1.1%
saint40075
 
1.1%
houston39455
 
1.1%
beach39263
 
1.1%
Other values (9456)3086433
82.9%

Most occurring characters

ValueCountFrequency (%)
a2502556
 
10.0%
e2221044
 
8.9%
n1890139
 
7.5%
o1878078
 
7.5%
l1661139
 
6.6%
i1634406
 
6.5%
r1492423
 
6.0%
t1316077
 
5.3%
s1161930
 
4.6%
876734
 
3.5%
Other values (55)8433156
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter20468277
81.7%
Uppercase Letter3721941
 
14.8%
Space Separator876734
 
3.5%
Dash Punctuation320
 
< 0.1%
Decimal Number255
 
< 0.1%
Other Punctuation155
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2502556
12.2%
e2221044
10.9%
n1890139
9.2%
o1878078
9.2%
l1661139
8.1%
i1634406
8.0%
r1492423
 
7.3%
t1316077
 
6.4%
s1161930
 
5.7%
d645556
 
3.2%
Other values (16)4064929
19.9%
Uppercase Letter
ValueCountFrequency (%)
S392746
 
10.6%
C362436
 
9.7%
M333442
 
9.0%
L266542
 
7.2%
B259083
 
7.0%
P242493
 
6.5%
A220680
 
5.9%
H200735
 
5.4%
R187703
 
5.0%
O160396
 
4.3%
Other values (16)1095685
29.4%
Decimal Number
ValueCountFrequency (%)
4162
63.5%
540
 
15.7%
719
 
7.5%
116
 
6.3%
65
 
2.0%
83
 
1.2%
23
 
1.2%
33
 
1.2%
02
 
0.8%
92
 
0.8%
Space Separator
ValueCountFrequency (%)
876734
100.0%
Dash Punctuation
ValueCountFrequency (%)
-320
100.0%
Other Punctuation
ValueCountFrequency (%)
.155
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin24190218
96.5%
Common877464
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2502556
 
10.3%
e2221044
 
9.2%
n1890139
 
7.8%
o1878078
 
7.8%
l1661139
 
6.9%
i1634406
 
6.8%
r1492423
 
6.2%
t1316077
 
5.4%
s1161930
 
4.8%
d645556
 
2.7%
Other values (42)7786870
32.2%
Common
ValueCountFrequency (%)
876734
99.9%
-320
 
< 0.1%
4162
 
< 0.1%
.155
 
< 0.1%
540
 
< 0.1%
719
 
< 0.1%
116
 
< 0.1%
65
 
< 0.1%
83
 
< 0.1%
23
 
< 0.1%
Other values (3)7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII25067682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2502556
 
10.0%
e2221044
 
8.9%
n1890139
 
7.5%
o1878078
 
7.5%
l1661139
 
6.6%
i1634406
 
6.5%
r1492423
 
6.0%
t1316077
 
5.3%
s1161930
 
4.6%
876734
 
3.5%
Other values (55)8433156
33.6%

County
Categorical

HIGH CARDINALITY

Distinct1707
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
Los Angeles
234122 
Miami-Dade
 
143939
Orange
 
114917
San Bernardino
 
55018
Dallas
 
50050
Other values (1702)
2247296 

Length

Max length20
Median length16
Mean length8.195874521
Min length3

Characters and Unicode

Total characters23320066
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)< 0.1%

Sample

1st rowFranklin
2nd rowMontgomery
3rd rowHamilton
4th rowSummit
5th rowHamilton

Common Values

ValueCountFrequency (%)
Los Angeles234122
 
8.2%
Miami-Dade143939
 
5.1%
Orange114917
 
4.0%
San Bernardino55018
 
1.9%
Dallas50050
 
1.8%
San Diego48366
 
1.7%
Sacramento46708
 
1.6%
Harris42559
 
1.5%
Riverside42176
 
1.5%
Montgomery41476
 
1.5%
Other values (1697)2026011
71.2%

Length

2022-05-28T12:19:38.875031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
los234123
 
6.6%
angeles234122
 
6.6%
san145652
 
4.1%
miami-dade143939
 
4.1%
orange114917
 
3.3%
bernardino55018
 
1.6%
dallas50050
 
1.4%
diego48366
 
1.4%
sacramento46708
 
1.3%
santa43738
 
1.2%
Other values (1668)2418952
68.4%

Most occurring characters

ValueCountFrequency (%)
a2602218
 
11.2%
e2445101
 
10.5%
n1899466
 
8.1%
o1619413
 
6.9%
r1442849
 
6.2%
i1307901
 
5.6%
s1284451
 
5.5%
l1157072
 
5.0%
t785883
 
3.4%
690243
 
3.0%
Other values (47)8085469
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18767444
80.5%
Uppercase Letter3684912
 
15.8%
Space Separator690243
 
3.0%
Dash Punctuation144233
 
0.6%
Other Punctuation33203
 
0.1%
Other Symbol31
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2602218
13.9%
e2445101
13.0%
n1899466
10.1%
o1619413
8.6%
r1442849
7.7%
i1307901
 
7.0%
s1284451
 
6.8%
l1157072
 
6.2%
t785883
 
4.2%
g678325
 
3.6%
Other values (17)3544765
18.9%
Uppercase Letter
ValueCountFrequency (%)
M414228
11.2%
S394862
10.7%
L372320
10.1%
D369042
10.0%
A336667
9.1%
C287055
 
7.8%
B212764
 
5.8%
H173937
 
4.7%
O173249
 
4.7%
P139348
 
3.8%
Other values (15)811440
22.0%
Other Punctuation
ValueCountFrequency (%)
.18431
55.5%
'14772
44.5%
Space Separator
ValueCountFrequency (%)
690243
100.0%
Dash Punctuation
ValueCountFrequency (%)
-144233
100.0%
Other Symbol
ValueCountFrequency (%)
31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22452356
96.3%
Common867710
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2602218
 
11.6%
e2445101
 
10.9%
n1899466
 
8.5%
o1619413
 
7.2%
r1442849
 
6.4%
i1307901
 
5.8%
s1284451
 
5.7%
l1157072
 
5.2%
t785883
 
3.5%
g678325
 
3.0%
Other values (42)7229677
32.2%
Common
ValueCountFrequency (%)
690243
79.5%
-144233
 
16.6%
.18431
 
2.1%
'14772
 
1.7%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII23319942
> 99.9%
None93
 
< 0.1%
Specials31
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2602218
 
11.2%
e2445101
 
10.5%
n1899466
 
8.1%
o1619413
 
6.9%
r1442849
 
6.2%
i1307901
 
5.6%
s1284451
 
5.5%
l1157072
 
5.0%
t785883
 
3.4%
690243
 
3.0%
Other values (45)8085345
34.7%
None
ValueCountFrequency (%)
ñ93
100.0%
Specials
ValueCountFrequency (%)
31
100.0%

State
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
CA
795868 
FL
401388 
TX
149037 
OR
126341 
VA
 
113535
Other values (44)
1259173 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5690684
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOH
2nd rowOH
3rd rowOH
4th rowOH
5th rowOH

Common Values

ValueCountFrequency (%)
CA795868
28.0%
FL401388
14.1%
TX149037
 
5.2%
OR126341
 
4.4%
VA113535
 
4.0%
NY108049
 
3.8%
PA99975
 
3.5%
MN97185
 
3.4%
NC91362
 
3.2%
SC89216
 
3.1%
Other values (39)773386
27.2%

Length

2022-05-28T12:19:39.056789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca795868
28.0%
fl401388
14.1%
tx149037
 
5.2%
or126341
 
4.4%
va113535
 
4.0%
ny108049
 
3.8%
pa99975
 
3.5%
mn97185
 
3.4%
nc91362
 
3.2%
sc89216
 
3.1%
Other values (39)773386
27.2%

Most occurring characters

ValueCountFrequency (%)
A1232010
21.6%
C1040681
18.3%
L515047
9.1%
N440972
 
7.7%
F401388
 
7.1%
T296934
 
5.2%
M267985
 
4.7%
O214529
 
3.8%
X149037
 
2.6%
I142296
 
2.5%
Other values (14)989805
17.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5690684
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1232010
21.6%
C1040681
18.3%
L515047
9.1%
N440972
 
7.7%
F401388
 
7.1%
T296934
 
5.2%
M267985
 
4.7%
O214529
 
3.8%
X149037
 
2.6%
I142296
 
2.5%
Other values (14)989805
17.4%

Most occurring scripts

ValueCountFrequency (%)
Latin5690684
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1232010
21.6%
C1040681
18.3%
L515047
9.1%
N440972
 
7.7%
F401388
 
7.1%
T296934
 
5.2%
M267985
 
4.7%
O214529
 
3.8%
X149037
 
2.6%
I142296
 
2.5%
Other values (14)989805
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5690684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1232010
21.6%
C1040681
18.3%
L515047
9.1%
N440972
 
7.7%
F401388
 
7.1%
T296934
 
5.2%
M267985
 
4.7%
O214529
 
3.8%
X149037
 
2.6%
I142296
 
2.5%
Other values (14)989805
17.4%

Zipcode
Categorical

HIGH CARDINALITY

Distinct363085
Distinct (%)12.8%
Missing1319
Missing (%)< 0.1%
Memory size21.7 MiB
91761
 
6162
33186
 
5248
92407
 
4528
92507
 
4527
91706
 
4471
Other values (363080)
2819087 

Length

Max length10
Median length5
Mean length6.409737193
Min length5

Characters and Unicode

Total characters18229440
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique209614 ?
Unique (%)7.4%

Sample

1st row43017
2nd row45424
3rd row45203
4th row44311
5th row45217

Common Values

ValueCountFrequency (%)
917616162
 
0.2%
331865248
 
0.2%
924074528
 
0.2%
925074527
 
0.2%
917064471
 
0.2%
331833518
 
0.1%
923243491
 
0.1%
328193455
 
0.1%
331693439
 
0.1%
917653189
 
0.1%
Other values (363075)2801995
98.5%

Length

2022-05-28T12:19:39.308487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
917616162
 
0.2%
331865248
 
0.2%
924074528
 
0.2%
925074527
 
0.2%
917064471
 
0.2%
331833518
 
0.1%
923243491
 
0.1%
328193455
 
0.1%
331693439
 
0.1%
917653189
 
0.1%
Other values (363075)2801995
98.5%

Most occurring characters

ValueCountFrequency (%)
02252352
12.4%
22174309
11.9%
32173091
11.9%
12043786
11.2%
91788058
9.8%
51575164
8.6%
71519116
8.3%
41485471
8.1%
61243905
6.8%
81172323
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17427575
95.6%
Dash Punctuation801865
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02252352
12.9%
22174309
12.5%
32173091
12.5%
12043786
11.7%
91788058
10.3%
51575164
9.0%
71519116
8.7%
41485471
8.5%
61243905
7.1%
81172323
6.7%
Dash Punctuation
ValueCountFrequency (%)
-801865
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common18229440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02252352
12.4%
22174309
11.9%
32173091
11.9%
12043786
11.2%
91788058
9.8%
51575164
8.6%
71519116
8.3%
41485471
8.1%
61243905
6.8%
81172323
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII18229440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02252352
12.4%
22174309
11.9%
32173091
11.9%
12043786
11.2%
91788058
9.8%
51575164
8.6%
71519116
8.3%
41485471
8.1%
61243905
6.8%
81172323
6.4%

Country
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
US
2845342 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5690684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US2845342
100.0%

Length

2022-05-28T12:19:39.475538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-28T12:19:39.625744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
us2845342
100.0%

Most occurring characters

ValueCountFrequency (%)
U2845342
50.0%
S2845342
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5690684
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U2845342
50.0%
S2845342
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5690684
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U2845342
50.0%
S2845342
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5690684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U2845342
50.0%
S2845342
50.0%

Timezone
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing3659
Missing (%)0.1%
Memory size21.7 MiB
US/Eastern
1221927 
US/Pacific
967094 
US/Central
488065 
US/Mountain
164597 

Length

Max length11
Median length10
Mean length10.05792237
Min length10

Characters and Unicode

Total characters28581427
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS/Eastern
2nd rowUS/Eastern
3rd rowUS/Eastern
4th rowUS/Eastern
5th rowUS/Eastern

Common Values

ValueCountFrequency (%)
US/Eastern1221927
42.9%
US/Pacific967094
34.0%
US/Central488065
 
17.2%
US/Mountain164597
 
5.8%
(Missing)3659
 
0.1%

Length

2022-05-28T12:19:39.759754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-28T12:19:39.945187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
us/eastern1221927
43.0%
us/pacific967094
34.0%
us/central488065
 
17.2%
us/mountain164597
 
5.8%

Most occurring characters

ValueCountFrequency (%)
U2841683
9.9%
S2841683
9.9%
/2841683
9.9%
a2841683
9.9%
i2098785
 
7.3%
n2039186
 
7.1%
c1934188
 
6.8%
t1874589
 
6.6%
e1709992
 
6.0%
r1709992
 
6.0%
Other values (9)5847963
20.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17214695
60.2%
Uppercase Letter8525049
29.8%
Other Punctuation2841683
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2841683
16.5%
i2098785
12.2%
n2039186
11.8%
c1934188
11.2%
t1874589
10.9%
e1709992
9.9%
r1709992
9.9%
s1221927
7.1%
f967094
 
5.6%
l488065
 
2.8%
Other values (2)329194
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
U2841683
33.3%
S2841683
33.3%
E1221927
14.3%
P967094
 
11.3%
C488065
 
5.7%
M164597
 
1.9%
Other Punctuation
ValueCountFrequency (%)
/2841683
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25739744
90.1%
Common2841683
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
U2841683
11.0%
S2841683
11.0%
a2841683
11.0%
i2098785
8.2%
n2039186
7.9%
c1934188
7.5%
t1874589
7.3%
e1709992
 
6.6%
r1709992
 
6.6%
s1221927
 
4.7%
Other values (8)4626036
18.0%
Common
ValueCountFrequency (%)
/2841683
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII28581427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U2841683
9.9%
S2841683
9.9%
/2841683
9.9%
a2841683
9.9%
i2098785
 
7.3%
n2039186
 
7.1%
c1934188
 
6.8%
t1874589
 
6.6%
e1709992
 
6.0%
r1709992
 
6.0%
Other values (9)5847963
20.5%

Airport_Code
Categorical

HIGH CARDINALITY

Distinct2004
Distinct (%)0.1%
Missing9549
Missing (%)0.3%
Memory size21.7 MiB
KCQT
 
52790
KMIA
 
45740
KORL
 
39380
KOPF
 
38556
KTMB
 
36250
Other values (1999)
2623077 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters11343172
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)< 0.1%

Sample

1st rowKOSU
2nd rowKFFO
3rd rowKLUK
4th rowKAKR
5th rowKLUK

Common Values

ValueCountFrequency (%)
KCQT52790
 
1.9%
KMIA45740
 
1.6%
KORL39380
 
1.4%
KOPF38556
 
1.4%
KTMB36250
 
1.3%
KEMT29931
 
1.1%
KRDU29322
 
1.0%
KFUL28575
 
1.0%
KHHR27305
 
1.0%
KBNA27061
 
1.0%
Other values (1994)2480883
87.2%

Length

2022-05-28T12:19:40.120398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kcqt52790
 
1.9%
kmia45740
 
1.6%
korl39380
 
1.4%
kopf38556
 
1.4%
ktmb36250
 
1.3%
kemt29931
 
1.1%
krdu29322
 
1.0%
kful28575
 
1.0%
khhr27305
 
1.0%
kbna27061
 
1.0%
Other values (1994)2480883
87.5%

Most occurring characters

ValueCountFrequency (%)
K2996056
26.4%
A594595
 
5.2%
C587310
 
5.2%
M541139
 
4.8%
S498222
 
4.4%
T476820
 
4.2%
L467659
 
4.1%
O449386
 
4.0%
R439134
 
3.9%
D437371
 
3.9%
Other values (26)3855480
34.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter11189338
98.6%
Decimal Number153834
 
1.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K2996056
26.8%
A594595
 
5.3%
C587310
 
5.2%
M541139
 
4.8%
S498222
 
4.5%
T476820
 
4.3%
L467659
 
4.2%
O449386
 
4.0%
R439134
 
3.9%
D437371
 
3.9%
Other values (16)3701646
33.1%
Decimal Number
ValueCountFrequency (%)
233233
21.6%
625327
16.5%
423061
15.0%
316538
10.8%
713339
8.7%
012452
 
8.1%
910951
 
7.1%
110758
 
7.0%
84389
 
2.9%
53786
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin11189338
98.6%
Common153834
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
K2996056
26.8%
A594595
 
5.3%
C587310
 
5.2%
M541139
 
4.8%
S498222
 
4.5%
T476820
 
4.3%
L467659
 
4.2%
O449386
 
4.0%
R439134
 
3.9%
D437371
 
3.9%
Other values (16)3701646
33.1%
Common
ValueCountFrequency (%)
233233
21.6%
625327
16.5%
423061
15.0%
316538
10.8%
713339
8.7%
012452
 
8.1%
910951
 
7.1%
110758
 
7.0%
84389
 
2.9%
53786
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII11343172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K2996056
26.4%
A594595
 
5.2%
C587310
 
5.2%
M541139
 
4.8%
S498222
 
4.4%
T476820
 
4.2%
L467659
 
4.1%
O449386
 
4.0%
R439134
 
3.9%
D437371
 
3.9%
Other values (26)3855480
34.0%

Weather_Timestamp
Categorical

HIGH CARDINALITY
MISSING

Distinct474214
Distinct (%)17.0%
Missing50736
Missing (%)1.8%
Memory size21.7 MiB
2021-12-17 14:53:00
 
640
2021-12-23 14:53:00
 
629
2021-01-26 15:53:00
 
598
2021-12-06 16:53:00
 
595
2021-12-03 16:53:00
 
593
Other values (474209)
2791551 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters53097514
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique186552 ?
Unique (%)6.7%

Sample

1st row2016-02-08 00:53:00
2nd row2016-02-08 05:58:00
3rd row2016-02-08 05:53:00
4th row2016-02-08 06:54:00
5th row2016-02-08 07:53:00

Common Values

ValueCountFrequency (%)
2021-12-17 14:53:00640
 
< 0.1%
2021-12-23 14:53:00629
 
< 0.1%
2021-01-26 15:53:00598
 
< 0.1%
2021-12-06 16:53:00595
 
< 0.1%
2021-12-03 16:53:00593
 
< 0.1%
2021-12-17 17:53:00572
 
< 0.1%
2021-11-30 16:53:00569
 
< 0.1%
2021-12-15 16:53:00567
 
< 0.1%
2021-12-03 14:53:00565
 
< 0.1%
2021-12-07 16:53:00560
 
< 0.1%
Other values (474204)2788718
98.0%
(Missing)50736
 
1.8%

Length

2022-05-28T12:19:40.343742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
16:53:0095753
 
1.7%
15:53:0094214
 
1.7%
14:53:0088219
 
1.6%
17:53:0085653
 
1.5%
13:53:0075857
 
1.4%
12:53:0066186
 
1.2%
18:53:0059874
 
1.1%
07:53:0055202
 
1.0%
11:53:0050706
 
0.9%
08:53:0046872
 
0.8%
Other values (3529)4870676
87.1%

Most occurring characters

ValueCountFrequency (%)
013312181
25.1%
27541273
14.2%
17373996
13.9%
-5589212
10.5%
:5589212
10.5%
53411025
 
6.4%
2794606
 
5.3%
32200904
 
4.1%
61217216
 
2.3%
41064424
 
2.0%
Other values (3)3003465
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39124484
73.7%
Dash Punctuation5589212
 
10.5%
Other Punctuation5589212
 
10.5%
Space Separator2794606
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013312181
34.0%
27541273
19.3%
17373996
18.8%
53411025
 
8.7%
32200904
 
5.6%
61217216
 
3.1%
41064424
 
2.7%
71029347
 
2.6%
91012235
 
2.6%
8961883
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
-5589212
100.0%
Other Punctuation
ValueCountFrequency (%)
:5589212
100.0%
Space Separator
ValueCountFrequency (%)
2794606
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common53097514
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
013312181
25.1%
27541273
14.2%
17373996
13.9%
-5589212
10.5%
:5589212
10.5%
53411025
 
6.4%
2794606
 
5.3%
32200904
 
4.1%
61217216
 
2.3%
41064424
 
2.0%
Other values (3)3003465
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII53097514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
013312181
25.1%
27541273
14.2%
17373996
13.9%
-5589212
10.5%
:5589212
10.5%
53411025
 
6.4%
2794606
 
5.3%
32200904
 
4.1%
61217216
 
2.3%
41064424
 
2.0%
Other values (3)3003465
 
5.7%

Temperature(F)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct788
Distinct (%)< 0.1%
Missing69274
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean61.79355592
Minimum-89
Maximum196
Zeros984
Zeros (%)< 0.1%
Negative6444
Negative (%)0.2%
Memory size21.7 MiB
2022-05-28T12:19:40.542380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-89
5-th percentile29
Q150
median64
Q376
95-th percentile88.9
Maximum196
Range285
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.62262938
Coefficient of variation (CV)0.3013684697
Kurtosis0.03002155249
Mean61.79355592
Median Absolute Deviation (MAD)13
Skewness-0.4910794711
Sum171543113.2
Variance346.8023252
MonotonicityNot monotonic
2022-05-28T12:19:40.756766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7364505
 
2.3%
7763575
 
2.2%
7560534
 
2.1%
7259681
 
2.1%
6858557
 
2.1%
6358259
 
2.0%
6457937
 
2.0%
7057760
 
2.0%
6656336
 
2.0%
5956025
 
2.0%
Other values (778)2182899
76.7%
(Missing)69274
 
2.4%
ValueCountFrequency (%)
-892
< 0.1%
-77.81
 
< 0.1%
-581
 
< 0.1%
-501
 
< 0.1%
-401
 
< 0.1%
-331
 
< 0.1%
-301
 
< 0.1%
-294
< 0.1%
-282
< 0.1%
-27.92
< 0.1%
ValueCountFrequency (%)
1963
 
< 0.1%
170.61
 
< 0.1%
168.81
 
< 0.1%
1561
 
< 0.1%
1441
 
< 0.1%
1361
 
< 0.1%
129.21
 
< 0.1%
127.41
 
< 0.1%
1208
< 0.1%
1195
< 0.1%

Wind_Chill(F)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct897
Distinct (%)< 0.1%
Missing469643
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean59.6582309
Minimum-89
Maximum196
Zeros1322
Zeros (%)< 0.1%
Negative20659
Negative (%)0.7%
Memory size21.7 MiB
2022-05-28T12:19:41.003754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-89
5-th percentile21
Q146
median63
Q376
95-th percentile88
Maximum196
Range285
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.16096744
Coefficient of variation (CV)0.354703234
Kurtosis0.3682111381
Mean59.6582309
Median Absolute Deviation (MAD)14
Skewness-0.7082722667
Sum141729999.5
Variance447.7865429
MonotonicityNot monotonic
2022-05-28T12:19:41.239118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7356492
 
2.0%
7753359
 
1.9%
7552898
 
1.9%
7252196
 
1.8%
6350784
 
1.8%
6450374
 
1.8%
7050187
 
1.8%
7948936
 
1.7%
6648929
 
1.7%
6848517
 
1.7%
Other values (887)1863027
65.5%
(Missing)469643
 
16.5%
ValueCountFrequency (%)
-892
< 0.1%
-801
 
< 0.1%
-65.91
 
< 0.1%
-591
 
< 0.1%
-581
 
< 0.1%
-53.54
< 0.1%
-53.11
 
< 0.1%
-52.31
 
< 0.1%
-51.71
 
< 0.1%
-51.52
< 0.1%
ValueCountFrequency (%)
1963
 
< 0.1%
1561
 
< 0.1%
1441
 
< 0.1%
1361
 
< 0.1%
1208
 
< 0.1%
1195
 
< 0.1%
11711
 
< 0.1%
1168
 
< 0.1%
11513
 
< 0.1%
11436
< 0.1%

Humidity(%)
Real number (ℝ≥0)

MISSING

Distinct100
Distinct (%)< 0.1%
Missing73092
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean64.36545225
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2022-05-28T12:19:41.476526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q148
median67
Q383
95-th percentile96
Maximum100
Range99
Interquartile range (IQR)35

Descriptive statistics

Standard deviation22.8745681
Coefficient of variation (CV)0.3553858056
Kurtosis-0.6827235834
Mean64.36545225
Median Absolute Deviation (MAD)17
Skewness-0.4157629378
Sum178437125
Variance523.2458658
MonotonicityNot monotonic
2022-05-28T12:19:41.690818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93103607
 
3.6%
10096907
 
3.4%
8760236
 
2.1%
9057587
 
2.0%
8953396
 
1.9%
8647702
 
1.7%
8246793
 
1.6%
8145330
 
1.6%
6745180
 
1.6%
9645111
 
1.6%
Other values (90)2170401
76.3%
(Missing)73092
 
2.6%
ValueCountFrequency (%)
126
 
< 0.1%
2121
 
< 0.1%
3341
 
< 0.1%
4943
 
< 0.1%
51804
 
0.1%
62408
0.1%
73457
0.1%
84182
0.1%
94758
0.2%
105601
0.2%
ValueCountFrequency (%)
10096907
3.4%
994306
 
0.2%
982329
 
0.1%
9729111
 
1.0%
9645111
1.6%
953342
 
0.1%
9439650
 
1.4%
93103607
3.6%
9223444
 
0.8%
9112828
 
0.5%

Pressure(in)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1068
Distinct (%)< 0.1%
Missing59200
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean29.47234438
Minimum0
Maximum58.9
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2022-05-28T12:19:41.903265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.25
Q129.31
median29.82
Q330.01
95-th percentile30.21
Maximum58.9
Range58.9
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.045286497
Coefficient of variation (CV)0.03546668983
Kurtosis16.72394531
Mean29.47234438
Median Absolute Deviation (MAD)0.26
Skewness-3.257540169
Sum82114136.52
Variance1.092623861
MonotonicityNot monotonic
2022-05-28T12:19:42.104544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.9643668
 
1.5%
29.9443165
 
1.5%
29.9942978
 
1.5%
30.0141736
 
1.5%
30.0341477
 
1.5%
30.0440651
 
1.4%
29.9740395
 
1.4%
3040178
 
1.4%
29.9540084
 
1.4%
29.9339474
 
1.4%
Other values (1058)2372336
83.4%
(Missing)59200
 
2.1%
ValueCountFrequency (%)
01
< 0.1%
0.021
< 0.1%
0.32
< 0.1%
2.992
< 0.1%
3.042
< 0.1%
16.722
< 0.1%
19.212
< 0.1%
19.242
< 0.1%
19.371
< 0.1%
19.481
< 0.1%
ValueCountFrequency (%)
58.91
 
< 0.1%
58.162
< 0.1%
58.131
 
< 0.1%
58.042
< 0.1%
57.741
 
< 0.1%
56.541
 
< 0.1%
56.311
 
< 0.1%
52.762
< 0.1%
38.943
< 0.1%
32.871
 
< 0.1%

Visibility(mi)
Real number (ℝ≥0)

MISSING

Distinct76
Distinct (%)< 0.1%
Missing70546
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean9.09939131
Minimum0
Maximum140
Zeros3238
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2022-05-28T12:19:42.316150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median10
Q310
95-th percentile10
Maximum140
Range140
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.717545745
Coefficient of variation (CV)0.298651377
Kurtosis103.5064331
Mean9.09939131
Median Absolute Deviation (MAD)0
Skewness3.113374137
Sum25248954.61
Variance7.385054877
MonotonicityNot monotonic
2022-05-28T12:19:42.510484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102230276
78.4%
779649
 
2.8%
968817
 
2.4%
855955
 
2.0%
553933
 
1.9%
649051
 
1.7%
246160
 
1.6%
445437
 
1.6%
344012
 
1.5%
138445
 
1.4%
Other values (66)63061
 
2.2%
(Missing)70546
 
2.5%
ValueCountFrequency (%)
03238
 
0.1%
0.06118
 
< 0.1%
0.1268
 
< 0.1%
0.12726
 
< 0.1%
0.1912
 
< 0.1%
0.22870
 
0.1%
0.2511359
0.4%
0.38130
 
< 0.1%
0.425
 
< 0.1%
0.512290
0.4%
ValueCountFrequency (%)
1402
 
< 0.1%
1301
 
< 0.1%
1204
 
< 0.1%
1111
 
< 0.1%
1101
 
< 0.1%
10037
 
< 0.1%
909
 
< 0.1%
80186
< 0.1%
7533
 
< 0.1%
7099
< 0.1%

Wind_Direction
Categorical

HIGH CORRELATION
MISSING

Distinct24
Distinct (%)< 0.1%
Missing73775
Missing (%)2.6%
Memory size21.7 MiB
CALM
433622 
S
 
169743
W
 
167830
WNW
 
145046
NW
 
141344
Other values (19)
1713982 

Length

Max length8
Median length5
Mean length2.708557289
Min length1

Characters and Unicode

Total characters7506948
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSW
2nd rowCalm
3rd rowCalm
4th rowCalm
5th rowWSW

Common Values

ValueCountFrequency (%)
CALM433622
15.2%
S169743
 
6.0%
W167830
 
5.9%
WNW145046
 
5.1%
NW141344
 
5.0%
SSW137282
 
4.8%
WSW130734
 
4.6%
SW128970
 
4.5%
SSE125516
 
4.4%
NNW124583
 
4.4%
Other values (14)1066897
37.5%

Length

2022-05-28T12:19:42.880573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
calm510156
18.4%
s169743
 
6.1%
w167830
 
6.1%
wnw145046
 
5.2%
nw141344
 
5.1%
ssw137282
 
5.0%
wsw130734
 
4.7%
sw128970
 
4.7%
sse125516
 
4.5%
nnw124583
 
4.5%
Other values (13)990363
35.7%

Most occurring characters

ValueCountFrequency (%)
W1290808
17.2%
S1206342
16.1%
N1046938
13.9%
E950572
12.7%
A537804
7.2%
C510156
 
6.8%
L433622
 
5.8%
M433622
 
5.8%
a144111
 
1.9%
t135911
 
1.8%
Other values (12)817062
10.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6640457
88.5%
Lowercase Letter866491
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a144111
16.6%
t135911
15.7%
l98763
11.4%
m76534
8.8%
o73553
8.5%
h73553
8.5%
s62358
7.2%
e61468
7.1%
r56607
 
6.5%
u39175
 
4.5%
Other values (2)44458
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
W1290808
19.4%
S1206342
18.2%
N1046938
15.8%
E950572
14.3%
A537804
8.1%
C510156
 
7.7%
L433622
 
6.5%
M433622
 
6.5%
V126411
 
1.9%
R104182
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin7506948
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W1290808
17.2%
S1206342
16.1%
N1046938
13.9%
E950572
12.7%
A537804
7.2%
C510156
 
6.8%
L433622
 
5.8%
M433622
 
5.8%
a144111
 
1.9%
t135911
 
1.8%
Other values (12)817062
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII7506948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W1290808
17.2%
S1206342
16.1%
N1046938
13.9%
E950572
12.7%
A537804
7.2%
C510156
 
6.8%
L433622
 
5.8%
M433622
 
5.8%
a144111
 
1.9%
t135911
 
1.8%
Other values (12)817062
10.9%

Wind_Speed(mph)
Real number (ℝ≥0)

MISSING
ZEROS

Distinct136
Distinct (%)< 0.1%
Missing157944
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean7.395044203
Minimum0
Maximum1087
Zeros433636
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2022-05-28T12:19:43.079393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median7
Q310
95-th percentile17
Maximum1087
Range1087
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation5.52745395
Coefficient of variation (CV)0.7474538081
Kurtosis1508.031366
Mean7.395044203
Median Absolute Deviation (MAD)3
Skewness10.24972664
Sum19873427
Variance30.55274717
MonotonicityNot monotonic
2022-05-28T12:19:43.288930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0433636
15.2%
5231000
 
8.1%
3225664
 
7.9%
6222502
 
7.8%
7205667
 
7.2%
8184037
 
6.5%
9165127
 
5.8%
10135850
 
4.8%
12117003
 
4.1%
1388872
 
3.1%
Other values (126)678040
23.8%
(Missing)157944
 
5.6%
ValueCountFrequency (%)
0433636
15.2%
158
 
< 0.1%
1.2116
 
< 0.1%
2150
 
< 0.1%
2.3251
 
< 0.1%
3225664
7.9%
3.542429
 
1.5%
4.644989
 
1.6%
5231000
8.1%
5.845427
 
1.6%
ValueCountFrequency (%)
10871
< 0.1%
9841
< 0.1%
822.82
< 0.1%
8121
< 0.1%
5182
< 0.1%
471.81
< 0.1%
245.11
< 0.1%
2431
< 0.1%
2321
< 0.1%
2111
< 0.1%

Precipitation(in)
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct230
Distinct (%)< 0.1%
Missing549458
Missing (%)19.3%
Infinite0
Infinite (%)0.0%
Mean0.00701693988
Minimum0
Maximum24
Zeros2104242
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2022-05-28T12:19:43.500984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.02
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09348831171
Coefficient of variation (CV)13.32323111
Kurtosis16665.25623
Mean0.00701693988
Median Absolute Deviation (MAD)0
Skewness106.2589449
Sum16110.08
Variance0.008740064427
MonotonicityNot monotonic
2022-05-28T12:19:43.708517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02104242
74.0%
0.0154160
 
1.9%
0.0226497
 
0.9%
0.0317786
 
0.6%
0.0413745
 
0.5%
0.0510563
 
0.4%
0.068575
 
0.3%
0.076868
 
0.2%
0.085942
 
0.2%
0.095303
 
0.2%
Other values (220)42203
 
1.5%
(Missing)549458
 
19.3%
ValueCountFrequency (%)
02104242
74.0%
0.0154160
 
1.9%
0.0226497
 
0.9%
0.0317786
 
0.6%
0.0413745
 
0.5%
0.0510563
 
0.4%
0.068575
 
0.3%
0.076868
 
0.2%
0.085942
 
0.2%
0.095303
 
0.2%
ValueCountFrequency (%)
245
 
< 0.1%
10.42
 
< 0.1%
10.051
 
< 0.1%
10.023
 
< 0.1%
10.011
 
< 0.1%
1015
 
< 0.1%
9.9950
< 0.1%
9.9810
 
< 0.1%
9.979
 
< 0.1%
9.9611
 
< 0.1%

Weather_Condition
Categorical

HIGH CARDINALITY
MISSING

Distinct127
Distinct (%)< 0.1%
Missing70636
Missing (%)2.5%
Memory size21.7 MiB
Fair
1107194 
Mostly Cloudy
363959 
Cloudy
348767 
Partly Cloudy
249939 
Clear
173823 
Other values (122)
531024 

Length

Max length35
Median length30
Mean length7.381590338
Min length3

Characters and Unicode

Total characters20481743
Distinct characters46
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st rowLight Rain
2nd rowLight Rain
3rd rowOvercast
4th rowOvercast
5th rowLight Rain

Common Values

ValueCountFrequency (%)
Fair1107194
38.9%
Mostly Cloudy363959
 
12.8%
Cloudy348767
 
12.3%
Partly Cloudy249939
 
8.8%
Clear173823
 
6.1%
Light Rain128403
 
4.5%
Overcast84882
 
3.0%
Scattered Clouds45132
 
1.6%
Light Snow43752
 
1.5%
Fog41226
 
1.4%
Other values (117)187629
 
6.6%
(Missing)70636
 
2.5%

Length

2022-05-28T12:19:43.934979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fair1122389
29.6%
cloudy979677
25.8%
mostly370256
 
9.8%
partly253815
 
6.7%
light193371
 
5.1%
rain185128
 
4.9%
clear173823
 
4.6%
overcast84882
 
2.2%
snow53748
 
1.4%
scattered45132
 
1.2%
Other values (51)328144
 
8.7%

Most occurring characters

ValueCountFrequency (%)
a1926734
 
9.4%
l1835427
 
9.0%
r1732864
 
8.5%
y1676635
 
8.2%
i1603157
 
7.8%
o1518576
 
7.4%
C1198634
 
5.9%
F1168184
 
5.7%
d1136664
 
5.5%
u1046822
 
5.1%
Other values (36)5638046
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15675963
76.5%
Uppercase Letter3735727
 
18.2%
Space Separator1015659
 
5.0%
Other Punctuation43608
 
0.2%
Dash Punctuation10786
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1926734
12.3%
l1835427
11.7%
r1732864
11.1%
y1676635
10.7%
i1603157
10.2%
o1518576
9.7%
d1136664
7.3%
u1046822
6.7%
t1035948
6.6%
s508898
 
3.2%
Other values (15)1654238
10.6%
Uppercase Letter
ValueCountFrequency (%)
C1198634
32.1%
F1168184
31.3%
M375193
 
10.0%
P256084
 
6.9%
L193372
 
5.2%
R185128
 
5.0%
S119203
 
3.2%
O84882
 
2.3%
H55817
 
1.5%
W46515
 
1.2%
Other values (8)52715
 
1.4%
Space Separator
ValueCountFrequency (%)
1015659
100.0%
Other Punctuation
ValueCountFrequency (%)
/43608
100.0%
Dash Punctuation
ValueCountFrequency (%)
-10786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19411690
94.8%
Common1070053
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1926734
9.9%
l1835427
9.5%
r1732864
 
8.9%
y1676635
 
8.6%
i1603157
 
8.3%
o1518576
 
7.8%
C1198634
 
6.2%
F1168184
 
6.0%
d1136664
 
5.9%
u1046822
 
5.4%
Other values (33)4567993
23.5%
Common
ValueCountFrequency (%)
1015659
94.9%
/43608
 
4.1%
-10786
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20481743
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1926734
 
9.4%
l1835427
 
9.0%
r1732864
 
8.5%
y1676635
 
8.2%
i1603157
 
7.8%
o1518576
 
7.4%
C1198634
 
5.9%
F1168184
 
5.7%
d1136664
 
5.5%
u1046822
 
5.1%
Other values (36)5638046
27.5%

Amenity
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2817352 
True
 
27990
ValueCountFrequency (%)
False2817352
99.0%
True27990
 
1.0%
2022-05-28T12:19:44.122670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Bump
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2844321 
True
 
1021
ValueCountFrequency (%)
False2844321
> 99.9%
True1021
 
< 0.1%
2022-05-28T12:19:44.270621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Crossing
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2645130 
True
 
200212
ValueCountFrequency (%)
False2645130
93.0%
True200212
 
7.0%
2022-05-28T12:19:44.410616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Give_Way
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2838474 
True
 
6868
ValueCountFrequency (%)
False2838474
99.8%
True6868
 
0.2%
2022-05-28T12:19:44.554652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Junction
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2554837 
True
290505 
ValueCountFrequency (%)
False2554837
89.8%
True290505
 
10.2%
2022-05-28T12:19:44.701652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

No_Exit
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2841048 
True
 
4294
ValueCountFrequency (%)
False2841048
99.8%
True4294
 
0.2%
2022-05-28T12:19:44.851648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Railway
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2822711 
True
 
22631
ValueCountFrequency (%)
False2822711
99.2%
True22631
 
0.8%
2022-05-28T12:19:45.005616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Roundabout
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2845219 
True
 
123
ValueCountFrequency (%)
False2845219
> 99.9%
True123
 
< 0.1%
2022-05-28T12:19:45.151857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Station
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2777347 
True
 
67995
ValueCountFrequency (%)
False2777347
97.6%
True67995
 
2.4%
2022-05-28T12:19:45.309820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Stop
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2794942 
True
 
50400
ValueCountFrequency (%)
False2794942
98.2%
True50400
 
1.8%
2022-05-28T12:19:45.463818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Traffic_Calming
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2843630 
True
 
1712
ValueCountFrequency (%)
False2843630
99.9%
True1712
 
0.1%
2022-05-28T12:19:45.595822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Traffic_Signal
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2580079 
True
265263 
ValueCountFrequency (%)
False2580079
90.7%
True265263
 
9.3%
2022-05-28T12:19:45.724590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Turning_Loop
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2845342 
ValueCountFrequency (%)
False2845342
100.0%
2022-05-28T12:19:45.857586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Sunrise_Sunset
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing2867
Missing (%)0.1%
Memory size21.7 MiB
Day
1811935 
Night
1030540 

Length

Max length5
Median length3
Mean length3.725100485
Min length3

Characters and Unicode

Total characters10588505
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowNight
3rd rowNight
4th rowNight
5th rowDay

Common Values

ValueCountFrequency (%)
Day1811935
63.7%
Night1030540
36.2%
(Missing)2867
 
0.1%

Length

2022-05-28T12:19:45.999622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-28T12:19:46.189781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
day1811935
63.7%
night1030540
36.3%

Most occurring characters

ValueCountFrequency (%)
D1811935
17.1%
a1811935
17.1%
y1811935
17.1%
N1030540
9.7%
i1030540
9.7%
g1030540
9.7%
h1030540
9.7%
t1030540
9.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7746030
73.2%
Uppercase Letter2842475
 
26.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1811935
23.4%
y1811935
23.4%
i1030540
13.3%
g1030540
13.3%
h1030540
13.3%
t1030540
13.3%
Uppercase Letter
ValueCountFrequency (%)
D1811935
63.7%
N1030540
36.3%

Most occurring scripts

ValueCountFrequency (%)
Latin10588505
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D1811935
17.1%
a1811935
17.1%
y1811935
17.1%
N1030540
9.7%
i1030540
9.7%
g1030540
9.7%
h1030540
9.7%
t1030540
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10588505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D1811935
17.1%
a1811935
17.1%
y1811935
17.1%
N1030540
9.7%
i1030540
9.7%
g1030540
9.7%
h1030540
9.7%
t1030540
9.7%

Civil_Twilight
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing2867
Missing (%)0.1%
Memory size21.7 MiB
Day
1929103 
Night
913372 

Length

Max length5
Median length3
Mean length3.642659654
Min length3

Characters and Unicode

Total characters10354169
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowNight
3rd rowNight
4th rowNight
5th rowDay

Common Values

ValueCountFrequency (%)
Day1929103
67.8%
Night913372
32.1%
(Missing)2867
 
0.1%

Length

2022-05-28T12:19:46.337814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-28T12:19:46.518566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
day1929103
67.9%
night913372
32.1%

Most occurring characters

ValueCountFrequency (%)
D1929103
18.6%
a1929103
18.6%
y1929103
18.6%
N913372
8.8%
i913372
8.8%
g913372
8.8%
h913372
8.8%
t913372
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7511694
72.5%
Uppercase Letter2842475
 
27.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1929103
25.7%
y1929103
25.7%
i913372
12.2%
g913372
12.2%
h913372
12.2%
t913372
12.2%
Uppercase Letter
ValueCountFrequency (%)
D1929103
67.9%
N913372
32.1%

Most occurring scripts

ValueCountFrequency (%)
Latin10354169
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D1929103
18.6%
a1929103
18.6%
y1929103
18.6%
N913372
8.8%
i913372
8.8%
g913372
8.8%
h913372
8.8%
t913372
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10354169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D1929103
18.6%
a1929103
18.6%
y1929103
18.6%
N913372
8.8%
i913372
8.8%
g913372
8.8%
h913372
8.8%
t913372
8.8%

Nautical_Twilight
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing2867
Missing (%)0.1%
Memory size21.7 MiB
Day
2063472 
Night
779003 

Length

Max length5
Median length3
Mean length3.54811599
Min length3

Characters and Unicode

Total characters10085431
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowNight
3rd rowNight
4th rowDay
5th rowDay

Common Values

ValueCountFrequency (%)
Day2063472
72.5%
Night779003
 
27.4%
(Missing)2867
 
0.1%

Length

2022-05-28T12:19:46.664532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-28T12:19:46.838530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
day2063472
72.6%
night779003
 
27.4%

Most occurring characters

ValueCountFrequency (%)
D2063472
20.5%
a2063472
20.5%
y2063472
20.5%
N779003
 
7.7%
i779003
 
7.7%
g779003
 
7.7%
h779003
 
7.7%
t779003
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7242956
71.8%
Uppercase Letter2842475
 
28.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2063472
28.5%
y2063472
28.5%
i779003
 
10.8%
g779003
 
10.8%
h779003
 
10.8%
t779003
 
10.8%
Uppercase Letter
ValueCountFrequency (%)
D2063472
72.6%
N779003
 
27.4%

Most occurring scripts

ValueCountFrequency (%)
Latin10085431
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D2063472
20.5%
a2063472
20.5%
y2063472
20.5%
N779003
 
7.7%
i779003
 
7.7%
g779003
 
7.7%
h779003
 
7.7%
t779003
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10085431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D2063472
20.5%
a2063472
20.5%
y2063472
20.5%
N779003
 
7.7%
i779003
 
7.7%
g779003
 
7.7%
h779003
 
7.7%
t779003
 
7.7%

Astronomical_Twilight
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing2867
Missing (%)0.1%
Memory size21.7 MiB
Day
2176983 
Night
665492 

Length

Max length5
Median length3
Mean length3.46824827
Min length3

Characters and Unicode

Total characters9858409
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowNight
3rd rowDay
4th rowDay
5th rowDay

Common Values

ValueCountFrequency (%)
Day2176983
76.5%
Night665492
 
23.4%
(Missing)2867
 
0.1%

Length

2022-05-28T12:19:46.987529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-28T12:19:47.161030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
day2176983
76.6%
night665492
 
23.4%

Most occurring characters

ValueCountFrequency (%)
D2176983
22.1%
a2176983
22.1%
y2176983
22.1%
N665492
 
6.8%
i665492
 
6.8%
g665492
 
6.8%
h665492
 
6.8%
t665492
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7015934
71.2%
Uppercase Letter2842475
28.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2176983
31.0%
y2176983
31.0%
i665492
 
9.5%
g665492
 
9.5%
h665492
 
9.5%
t665492
 
9.5%
Uppercase Letter
ValueCountFrequency (%)
D2176983
76.6%
N665492
 
23.4%

Most occurring scripts

ValueCountFrequency (%)
Latin9858409
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D2176983
22.1%
a2176983
22.1%
y2176983
22.1%
N665492
 
6.8%
i665492
 
6.8%
g665492
 
6.8%
h665492
 
6.8%
t665492
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII9858409
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D2176983
22.1%
a2176983
22.1%
y2176983
22.1%
N665492
 
6.8%
i665492
 
6.8%
g665492
 
6.8%
h665492
 
6.8%
t665492
 
6.8%

Interactions

2022-05-28T12:15:00.915778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:36.743917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:55.700328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:13.734254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:31.911811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:51.547460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:07.531498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:17.412218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:35.193923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:51.830224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:10.015463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:27.569124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:44.632856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:15:02.206902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:38.695930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:57.218604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:15.229192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:33.428332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:52.915945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:08.253712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:18.848290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:36.552220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:53.401318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:11.416507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:29.014592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:46.028855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:15:03.310909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:40.416123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:58.654594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:16.687464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:36.637835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:54.318523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:08.976830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:20.353338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:37.876208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:54.845365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:12.866838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:30.459101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:47.337144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:15:04.584294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:41.933742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:00.144831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:18.313661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:38.126417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:55.630190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:09.701131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:21.854326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:39.157200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:56.356941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:14.346884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:31.816483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:48.668190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:15:05.928313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:43.329533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:01.612875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:19.711065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:39.513865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:56.916178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:10.369914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:23.271256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:40.444600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:57.821997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:15.680873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:33.242841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:50.084598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:15:06.523778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:44.113107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:02.341579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:20.498426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:40.261096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:57.675562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:11.221258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:23.987177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:41.177596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:58.556175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:16.419817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:33.951429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:50.730459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:15:07.581933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:45.611091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:03.782986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:22.049501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:41.751120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:59.044671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:11.888627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:25.497500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:42.484712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:00.042293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:17.864415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:35.332759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:52.110651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:15:08.670768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:46.924473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:05.153373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:23.352138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:43.057479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:00.265165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:12.576616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:26.757341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:43.713700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:01.419079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:19.119404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:36.571801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:53.384958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:15:09.830782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:48.448569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:06.609762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:24.845556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:44.560697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:01.656121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:13.300364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:28.277474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:45.282876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:02.877029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:20.581548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:38.030028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:54.674143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:15:10.897828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:50.005861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:08.099788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:26.356771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:46.062702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:02.998977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:13.975365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:29.759286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:46.564616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:04.389541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:21.995446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:39.376924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:56.038399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:15:11.986820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:51.512468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:09.601796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:27.763301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:47.514735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:04.365682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:14.621356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:31.218272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:47.844552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:05.895164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:23.384733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:40.733702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:57.419628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:15:13.039811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:53.003975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:11.015728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:29.221272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:48.932867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:05.675897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:15.283625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:32.645408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:49.174440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:07.281756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:24.944175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:42.142148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:58.647741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:15:14.055033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:11:54.280596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:12.251993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:30.460837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:12:50.180737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:06.802943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:15.889232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:33.916717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:13:50.381968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:08.554353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:26.160182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:43.297504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-28T12:14:59.753731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-28T12:19:47.522568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-28T12:19:48.006526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-28T12:19:48.502559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-28T12:19:48.986773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-28T12:19:49.438768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-28T12:15:30.795266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-28T12:16:58.265279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-28T12:18:56.015444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-28T12:19:09.194117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IDSeverityStart_TimeEnd_TimeStart_LatStart_LngEnd_LatEnd_LngDistance(mi)DescriptionNumberStreetSideCityCountyStateZipcodeCountryTimezoneAirport_CodeWeather_TimestampTemperature(F)Wind_Chill(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Precipitation(in)Weather_ConditionAmenityBumpCrossingGive_WayJunctionNo_ExitRailwayRoundaboutStationStopTraffic_CalmingTraffic_SignalTurning_LoopSunrise_SunsetCivil_TwilightNautical_TwilightAstronomical_Twilight
0A-132016-02-08 00:37:082016-02-08 06:37:0840.108910-83.09286040.112060-83.0318703.230Between Sawmill Rd/Exit 20 and OH-315/Olentangy Riv Rd/Exit 22 - Accident.NaNOuterbelt ERDublinFranklinOH43017USUS/EasternKOSU2016-02-08 00:53:0042.136.158.029.7610.0SW10.40.00Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseNightNightNightNight
1A-222016-02-08 05:56:202016-02-08 11:56:2039.865420-84.06280039.865010-84.0487300.747At OH-4/OH-235/Exit 41 - Accident.NaNI-70 ERDaytonMontgomeryOH45424USUS/EasternKFFO2016-02-08 05:58:0036.9NaN91.029.6810.0CalmNaN0.02Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseNightNightNightNight
2A-322016-02-08 06:15:392016-02-08 12:15:3939.102660-84.52468039.102090-84.5239600.055At I-71/US-50/Exit 1 - Accident.NaNI-75 SRCincinnatiHamiltonOH45203USUS/EasternKLUK2016-02-08 05:53:0036.0NaN97.029.7010.0CalmNaN0.02OvercastFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseNightNightNightDay
3A-422016-02-08 06:51:452016-02-08 12:51:4541.062130-81.53784041.062170-81.5354700.123At Dart Ave/Exit 21 - Accident.NaNI-77 NRAkronSummitOH44311USUS/EasternKAKR2016-02-08 06:54:0039.0NaN55.029.6510.0CalmNaNNaNOvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseNightNightDayDay
4A-532016-02-08 07:53:432016-02-08 13:53:4339.172393-84.49279239.170476-84.5017980.500At Mitchell Ave/Exit 6 - Accident.NaNI-75 SRCincinnatiHamiltonOH45217USUS/EasternKLUK2016-02-08 07:53:0037.029.893.029.6910.0WSW10.40.01Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
5A-622016-02-08 08:16:572016-02-08 14:16:5739.063240-84.03243039.067310-84.0585101.427At Dela Palma Rd - Accident.NaNState Route 32RWilliamsburgClermontOH45176USUS/EasternKI692016-02-08 08:16:0035.629.2100.029.6610.0WSW8.1NaNOvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseDayDayDayDay
6A-722016-02-08 08:15:412016-02-08 14:15:4139.775650-84.18603039.772750-84.1880500.227At OH-4/Exit 54 - Accident.NaNI-75 SRDaytonMontgomeryOH45404USUS/EasternKFFO2016-02-08 08:18:0033.8NaN100.029.633.0SW2.3NaNMostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
7A-822016-02-08 11:51:462016-02-08 17:51:4641.375310-81.82017041.367860-81.8217400.521At Bagley Rd/Exit 235 - Accident.NaNI-71 SRClevelandCuyahogaOH44130USUS/EasternKCLE2016-02-08 11:51:0033.130.092.029.630.5SW3.50.08SnowFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
8A-922016-02-08 14:19:572016-02-08 20:19:5740.702247-84.07588740.699110-84.0842930.491At OH-65/Exit 122 - Accident.NaNE Hanthorn RdRLimaAllenOH45806USUS/EasternKAOH2016-02-08 13:53:0039.031.870.029.5910.0WNW11.5NaNOvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
9A-1022016-02-08 15:16:432016-02-08 21:16:4340.109310-82.96849040.110780-82.9840000.826At I-71/Exit 26 - Accident.NaNOuterbelt WRWestervilleFranklinOH43081USUS/EasternKCMH2016-02-08 15:12:0032.028.7100.029.590.5West3.50.05SnowFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay

Last rows

IDSeverityStart_TimeEnd_TimeStart_LatStart_LngEnd_LatEnd_LngDistance(mi)DescriptionNumberStreetSideCityCountyStateZipcodeCountryTimezoneAirport_CodeWeather_TimestampTemperature(F)Wind_Chill(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Precipitation(in)Weather_ConditionAmenityBumpCrossingGive_WayJunctionNo_ExitRailwayRoundaboutStationStopTraffic_CalmingTraffic_SignalTurning_LoopSunrise_SunsetCivil_TwilightNautical_TwilightAstronomical_Twilight
2845332A-284533322019-08-23 17:42:272019-08-23 18:11:1034.064460-118.00388034.065330-117.9971500.390At I-605 - Accident.NaNI-10 ERBaldwin ParkLos AngelesCA91706USUS/PacificKEMT2019-08-23 17:53:0078.078.052.029.6910.0VAR6.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
2845333A-284533422019-08-23 17:40:122019-08-23 18:08:3533.943599-117.07788033.943599-117.0778800.000At Jack Rabbit Trl - Accident.NaNCA-60 ERMoreno ValleyRiversideCA92555USUS/PacificKRIV2019-08-23 17:58:0088.088.032.028.2010.0WNW10.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
2845334A-284533522019-08-23 17:40:122019-08-23 18:08:3534.261030-119.22800034.262390-119.2308700.189At Telephone Rd/Exit 65 - Accident.NaNEl Camino Real NRVenturaVenturaCA93003USUS/PacificKOXR2019-08-23 17:51:0073.073.068.029.7610.0W9.00.0FairFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
2845335A-284533622019-08-23 17:43:562019-08-23 18:12:2733.741700-117.83709033.739170-117.8300100.443At CA-55 - Accident.NaNSanta Ana Fwy SRTustinOrangeCA92780USUS/PacificKSNA2019-08-23 17:53:0075.075.060.029.7410.0SSW9.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
2845336A-284533722019-08-23 18:30:232019-08-23 18:58:5434.239104-118.41617634.239104-118.4161760.000At Osborne St/Exit 154 - Accident.NaNGolden State Fwy NRPacoimaLos AngelesCA91331USUS/PacificKWHP2019-08-23 18:50:0081.081.048.028.7810.0ESE6.0NaNFairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
2845337A-284533822019-08-23 18:03:252019-08-23 18:32:0134.002480-117.37936033.998880-117.3709400.543At Market St - Accident.NaNPomona Fwy ERRiversideRiversideCA92501USUS/PacificKRAL2019-08-23 17:53:0086.086.040.028.9210.0W13.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
2845338A-284533922019-08-23 19:11:302019-08-23 19:38:2332.766960-117.14806032.765550-117.1536300.338At Camino Del Rio/Mission Center Rd - Accident.NaNI-8 WRSan DiegoSan DiegoCA92108USUS/PacificKMYF2019-08-23 18:53:0070.070.073.029.3910.0SW6.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
2845339A-284534022019-08-23 19:00:212019-08-23 19:28:4933.775450-117.84779033.777400-117.8572700.561At Glassell St/Grand Ave - Accident. in the right lane.NaNGarden Grove FwyROrangeOrangeCA92866USUS/PacificKSNA2019-08-23 18:53:0073.073.064.029.7410.0SSW10.00.0Partly CloudyFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
2845340A-284534122019-08-23 19:00:212019-08-23 19:29:4233.992460-118.40302033.983110-118.3956500.772At CA-90/Marina Fwy/Jefferson Blvd - Accident.NaNSan Diego Fwy SRCulver CityLos AngelesCA90230USUS/PacificKSMO2019-08-23 18:51:0071.071.081.029.6210.0SW8.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
2845341A-284534222019-08-23 18:52:062019-08-23 19:21:3134.133930-117.23092034.137360-117.2393400.537At Highland Ave/Arden Ave - Accident.NaNCA-210 WRHighlandSan BernardinoCA92346USUS/PacificKSBD2019-08-23 20:50:0079.079.047.028.637.0SW7.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay